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Forecasting daily bathtub-drowning mortality in Japan: a comparative analysis of statistical, machine learning, and deep learning approaches.

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Title: Forecasting daily bathtub-drowning mortality in Japan: a comparative analysis of statistical, machine learning, and deep learning approaches.
Authors: Tai Y; Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan., Obayashi K; Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan., Yamagami Y; Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan., Saeki K; Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan.
Source: Frontiers in public health [Front Public Health] 2025 Dec 10; Vol. 13, pp. 1715622. Date of Electronic Publication: 2025 Dec 10 (Print Publication: 2025).
Publication Type: Journal Article; Comparative Study
Journal Info: Publisher: Frontiers Editorial Office Country of Publication: Switzerland NLM ID: 101616579 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-2565 (Electronic) Linking ISSN: 22962565 NLM ISO Abbreviation: Front Public Health Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: <searchLink fieldCode="AU" term="%22Tai+Y%22">Tai Y</searchLink>; Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan.<br /><searchLink fieldCode="AU" term="%22Obayashi+K%22">Obayashi K</searchLink>; Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan.<br /><searchLink fieldCode="AU" term="%22Yamagami+Y%22">Yamagami Y</searchLink>; Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan.<br /><searchLink fieldCode="AU" term="%22Saeki+K%22">Saeki K</searchLink>; Department of Epidemiology, Nara Medical University School of Medicine, Nara, Japan.
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  Data: <searchLink fieldCode="JN" term="%22101616579%22">Frontiers in public health</searchLink> [Front Public Health] 2025 Dec 10; Vol. 13, pp. 1715622. <i>Date of Electronic Publication: </i>2025 Dec 10 (<i>Print Publication: </i>2025).
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Frontiers+Editorial+Office%22">Frontiers Editorial Office </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>101616579 <i>Publication Model: </i>eCollection <i>Cited Medium: </i>Internet <i>ISSN: </i>2296-2565 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2222962565%22">22962565 </searchLink><i>NLM ISO Abbreviation: </i>Front Public Health <i>Subsets: </i>MEDLINE
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        Value: 10.3389/fpubh.2025.1715622
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      – Code: eng
        Text: English
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        StartPage: 1715622
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      – TitleFull: Forecasting daily bathtub-drowning mortality in Japan: a comparative analysis of statistical, machine learning, and deep learning approaches.
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            NameFull: Tai Y
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            NameFull: Obayashi K
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            – D: 10
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              Text: 2025 Dec 10
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              Y: 2025
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